Impact Factor 3.517 | CiteScore 3.60
More on impact ›

Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2019.00911

Optimization techniques to deeply mine the transcriptomic profile of the sub-genomes in hybrid fish lineage

 Zhong Wan1*,  Jiayi Tang2, Li Ren3, Yamei Xiao3 and  Shaojun Liu3
  • 1Central South University, China
  • 2School of Mathematics and Statistics, Central South University, China
  • 3State Key Laboratory of Developmental Biology of Freshwater Fish, Hunan Normal University, China

It has been shown that reciprocal cross allodiploid lineage with sub-genomes derived from the cross of \emph{Megalobrama amblycephala} (BSB)$\times$ \emph{Culter alburnus} (TC) generates the variations in phenotypes and genotypes, but it is still a challenge to deeply mine biological information in the transcriptomic profile of this lineage owing to its genomic complexity and lack of efficient data mining methods. In this paper, we establish an optimization model by non-negative matrix factorization approach for deeply mining the transcriptomic profile of the sub-genomes in hybrid fish lineage. A new so-called spectral conjugate gradient algorithm is developed to solve a sequence of large-scale subproblems such that the original complicated model can be efficiently solved. It is shown that the proposed method can provide a satisfactory result of taxonomy for the hybrid fish lineage such that their genetic characteristics are revealed, even for the samples with larger detection errors. Particularly, highly expressed shared genes are found for each class of the fish. The hybrid progeny of
TC and BSB displays significant hybrid characteristics. The third generation of TC-BSB hybrid progeny (BT$_{F_3}$ and TB$_{F_3}$) shows larger trait separation.

Keywords: gene expression profile, Optimization model, classification of hybrid fishes, algorithm, fish, nonnegative matrix factorization

Received: 02 May 2019; Accepted: 29 Aug 2019.

Copyright: © 2019 Wan, Tang, Ren, Xiao and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Zhong Wan, Central South University, Changsha, China,